Potentially Divergent Paths in the AI Era? A Mixed-Method Policy Analysis of Artificial Intelligence Integration Frameworks Across Texas Hispanic-Serving Institutions
DOI:
https://doi.org/10.53761/ssw9kz69Keywords:
artificial intelligence policy, Hispanic-Serving Institutions, higher education governance, institutional policy analysis, educational equity, policy diffusion networksAbstract
As artificial intelligence (AI) transforms higher education, institutions face critical challenges in developing appropriate governance frameworks, particularly in Hispanic-Serving Institutions (HSIs) where technological integration intersects with educational equity concerns. Drawing on institutional isomorphism theory and the framework of policy diffusion, this study conducts a systematic comparative analysis of artificial intelligence (AI) integration policies across nineteen Hispanic-Serving Institutions (HSIs) in Texas, revealing potentially divergent institutional approaches to AI governance in higher education. Through comprehensive document analysis incorporating qualitative content analysis, policy network analysis, and quantitative scoring methods, the research examines how different types of institutions are developing and implementing AI integration frameworks at the institutional policy level. The analysis reveals seven distinct policy approaches: Holistic Integration, Research and Innovation Driven, Teaching and Learning Focused, Ethically Anchored, Community and Workforce Oriented, Cautious Adoption, and Emerging Framework. Policy network analysis demonstrates strong policy diffusion patterns among institutions within the same university systems and geographic regions, while quantitative scoring indicates significant disparities in policy comprehensiveness between research-intensive universities and other institutions. The findings suggest that institutional characteristics, including research classification, size, and resources, significantly influence institutional AI policy development approaches. Notably, the study identifies a potential policy development gap between research-intensive universities and teaching-focused institutions or community colleges, raising important questions about equity in AI integration across different institutional types. These findings provide crucial insights for policymakers and institutional leaders working to develop effective AI integration frameworks, particularly in the context of serving historically underrepresented student populations in higher education.
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Copyright (c) 2024 Dr. Chunling Niu, Soheila Sadeghi, Dr. Jin Rui, Dr. Stephanie Grote-Garcia, Dr. Ashley Love, Mr. Brian Waltman, Dr. Theresa Garfield, Dr. Arthur Hernandez (Author)
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